CN113626550A - Image tile map service method based on triple bidirectional index and optimized cache - Google Patents
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Abstract
The invention discloses an image tile map service method based on triple bidirectional indexing and optimized caching, and relates to the field of organization management and retrieval browsing of remote sensing images. The method comprises the following steps: establishing a multi-level geographic grid system, and numbering the geographic grids; extracting metadata of the remote sensing image and storing the metadata in a database; slicing the remote sensing image according to a multi-level geographic grid system and an optimized cache strategy to generate a core level tile, and storing the core level tile in a database; establishing a bidirectional index of the triples; when receiving a user request, searching the database according to the index, and if the requested tile exists, directly returning the tile and related metadata; and if the tiles do not exist, generating the tiles in real time as required, and returning and storing the generated tiles and related metadata into the database. The problem of index under the remote sensing image map service lacks metadata support is solved, and the problem of unbalanced coordination on response speed and occupied space in full cache and real-time cache is solved.
Description
Technical Field
The invention relates to the field of organization management, retrieval and browsing of remote sensing images, in particular to a remote sensing image tile map service method based on { geographic grid, metadata, tile } triple mutual bidirectional indexing and optimized caching technology.
Background
With the rapid development of remote sensing technology, remote sensing image data is growing explosively. How to carry out efficient organization management, retrieval and browsing and the like on the massive remote sensing data so as to more conveniently and quickly serve the application of the industries such as disaster emergency, environment, resources, agriculture and the like, and becomes very urgent common business requirements and technical requirements.
The traditional remote sensing image organization management and use mode taking 'scene' as a unit has the problems of various data formats, heavy files, the need of opening professional software and the like, and the high efficiency and large-scale popularization and utilization of the remote sensing image are severely restricted.
The current mainstream tile map service technology is a remote sensing image organization management mode based on a geographic grid, a remote sensing image is processed into multi-level tile data, and when a user requests a certain area, the tile data of the corresponding level in the current range is searched and transmitted according to needs, so that the searching calculation time and the data transmission quantity are reduced. Although this method can significantly improve the efficiency of remote sensing image retrieval and access, the following problems mainly exist:
(1) whether a pre-slicing technical scheme or a real-time slicing scheme is adopted, good balanced coordination cannot be achieved in response speed and occupied space. The pre-slicing scheme needs to divide the remote sensing image into multiple levels of tiles in advance according to a geographic grid system, which requires a large preprocessing time, and the generated tile data also occupies an extra storage space (the tiles of the whole level are about 1.5 times of those of the original remote sensing image). According to the real-time slicing scheme, according to an area requested by a user, an original remote sensing image of the area is read in real time and is sliced to generate tiles to be returned to the user. This scheme is simple and flexible, but the response speed of retrieval access is slower than that of the pre-slicing scheme, and it occupies a large amount of memory and computing resources, and the supportable concurrency is limited.
(2) In addition, because the organization and management are completely carried out according to the tiles based on the geographic grids, the data information of the remote sensing image elements with rich time, sensors, coverage space range, resolution ratio and the like taking scenes as units is lost, thereby restricting richer and more flexible retrieval access and application modes.
Disclosure of Invention
In view of the urgent need of the remote sensing monitoring field for the portable remote sensing image map service and the defects of the current remote sensing image map service technology, a remote sensing image tile map service method based on { geography grid, metadata, tile } triple mutual bidirectional indexing and optimized caching technology is researched and established, and the problems of rich convenient indexing, full caching and unbalanced coordination on response speed and occupied space of real-time caching under the condition that the current remote sensing image map service lacks metadata support are mainly solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
an image tile map service method based on triple bidirectional indexing and optimized caching comprises the following steps:
establishing a multi-level geographic grid system, wherein the multi-level geographic grid system comprises a plurality of layers of geographic grids, and numbering the geographic grids;
extracting metadata of the remote sensing image and storing the metadata into a metadata database;
according to the multi-level geographic grid system and the optimized cache strategy, carrying out slice pretreatment on the remote sensing image to form a best core level tile subjected to pretreatment, and storing the best core level tile in a tile database;
establishing a mutual bidirectional index of the triples formed by the { geography grids, the metadata and the tiles };
receiving request information sent by a user, retrieving the request according to the established index, and directly returning the request tile and related metadata information if the request tile exists; if the request tiles do not exist, the tiles of the nearest deep level available in the database are used for calculating and generating the required tiles in real time according to needs, the generated tiles and related metadata information are returned, and meanwhile, the tiles are stored in the tile database and are directly called and returned when the next request is made.
Preferably, the geographic grid and the tiles are indexes of each other, the metadata and the tiles are indexes of each other, and the geographic grid and the metadata are indexes of each other.
Preferably, the specific steps for establishing the multi-level geographic grid system are as follows:
extending the longitude range (-180 degrees, 180 degrees) and the latitude range (-90 degrees, 90 degrees) to the geographical range of (-256 degrees, 256 degrees), dividing the geographical range into 4 grids according to a central horizontal line and a central vertical line, wherein the length and the width of each grid are 256-degree squares, and forming a layer 1 grid; on the basis of the grid of the previous layer, gradually dividing each grid of the previous layer into 4 equal parts according to the horizontal central line and the vertical central line of the grid to respectively form 2-N layers of grids;
each mesh of each layer of meshes is uniquely numbered.
Preferably, the metadata includes a unique number of the related picture, a picture time, a sensor name, a coverage, a resolution, and the like.
Preferably, the preprocessing of the remote sensing image slice specifically comprises:
and taking the established multi-level geographic grid system as a slicing reference, carrying out slicing pretreatment on each scene remote sensing image taking the scene as a unit to form a tile, and storing the tile in a tile database. (1) For each scene remote sensing image, the slice preprocessing of the whole level is not carried out, and the L-th slice preprocessing is carried out at mostMaxHierarchy, LMidHierarchy, LFullPreprocessing 3 most core-level slices of the level, generating 3 levels of tiles of the scene remote sensing image at most in advance, and reading the original remote sensing image for 3 times in the 3 times of slice processing at most; (2) and the tiles of the rest layers are generated based on the nearest deep layer tiles through real-time up-sampling calculation, and the tiles generated in real time are stored, and a subsequent request is directly read without calculation generation again, so that the speed of a subsequent response is improved.
And when the tile corresponding to a certain grid of a certain level in the remote sensing image cannot completely cover the whole grid, the uncovered area of the tile is filled with transparent color.
Preferably, the specific steps for responding to the request are as follows:
a user sends request information, wherein the request information contains information such as a required space range, a required time range, a sensor and the like;
after receiving the request information, the server analyzes the request and calculates to obtain the related grid level and the range of the row number and the column number;
the server inquires whether the tiles meeting the conditions exist in a server database in the background according to the analyzed grid level, the line and row number range and the image time information, and returns a tile number list of the related grid meeting the conditions respectively:
if the tiles of the grid exist, directly reading tile data and related metadata information to return based on the tile numbers;
if a tile for the grid does not exist, querying the nearest neighboring deep level tile currently available; obtaining a tile corresponding to the nearest deep level, and performing calculation processing in real time to generate a tile of a required level; and returning the generated tile, and simultaneously storing the generated tile and related information thereof in a tile database for subsequent access and direct call return.
According to the technical scheme, compared with the prior art, the image tile map service method based on triple bidirectional indexing and optimized caching solves the problems that the existing remote sensing image map service lacks of indexing under metadata support and is unbalanced and coordinated in response speed and occupied space in full caching and real-time caching, so that the workload, time and occupied resources of preprocessing are effectively reduced, meanwhile, the method has rich and flexible index access modes and efficient response performance under the metadata support, and large-scale, flexible, diverse and efficient access and application can be supported.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a plan view of a multi-level geogrid grid architecture establishment strategy of the present invention;
FIG. 3 is a block diagram of a tile organization management and index framework based on triple bi-directional indexing according to the present invention;
fig. 4 is a diagram of the tile request response strategy of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses an image tile map service method based on triple bidirectional indexing and optimized caching, which comprises the following steps as shown in figures 1 and 2:
establishing a multi-level geographic grid system, wherein the multi-level geographic grid system comprises a plurality of layers of geographic grids, and numbering the geographic grids;
extracting metadata of the remote sensing image and storing the metadata into a metadata database;
preprocessing the remote sensing image slices according to the multi-level geographic grid system and an optimized cache strategy to form preprocessed best core level tile data, and storing the preprocessed best core level tile data in a tile database;
establishing a mutual bidirectional index of triples formed by the geographic grids, the metadata and the tiles;
receiving request information sent by a user, retrieving the request according to the established index, and directly returning the request tile and related metadata information if the request tile exists; and if the request tiles do not exist, generating the tiles in real time as required, returning the generated tiles and the related metadata information, and simultaneously storing the tiles into a tile database for direct calling and returning when the next request is made.
In this embodiment, the specific contents are as follows:
establishment of multi-stage geogrid system
Gridded management is an important way to organize, manage and index spatial data. In view of the spatial property of the remote sensing image and the different display characteristics of the remote sensing image under different scales, the following grid system is specially designed:
A. layer 1 geogrid mesh: the longitude range (-180 °, 180 °) and the latitude range (-90 °, 90 °) are both extended to the geographical range (-256 °, 256 °), and divided into 4 grids according to the central horizontal line (central meridian) and the vertical line (central meridian), and each grid has a length and width of a square of 256 °, thereby forming a layer 1 grid. The latitude and longitude are expanded to the range of (-256 degrees and 256 degrees), and the latitude and longitude can be matched with the size of a general tile (generally, 256 × 256 pixel tiles are suitable and have good performance), and the latitude and longitude range and the processing mode can be unified to avoid difference.
B. Layer 2 to layer N geogrids: on the basis of the grid of the previous layer, each grid of the previous layer is gradually divided into 4 equal parts according to the horizontal central line and the vertical central line of the grid, and 2-N layers of grids are respectively formed. Wherein N is the maximum subdivision level depth of the geographic grid system. And N can be determined according to the highest resolution and service requirements of all remote sensing images. The deeper the hierarchy, the greater the number of meshes for that hierarchy. The calculation formula of the number of grids in the L-th grid is as follows: gridnum (l) ═ 4LWherein L is the number of grid layers. For example, for a 4 th layer of mesh, where the number of meshes is 44256 are provided.
C. The grids are numbered. Each grid in the geographic grid system is uniquely numbered according to the numbering rule LLYYYYYYXXXXX, wherein LL of the first two digits represents the hierarchy number of the grid, YYYYYYY of the middle six digits represents the row number of the grid, and XXXXXXX of the last six digits represents the column number of the grid. The row number and the column number are both counted from the grid (the row number and the column number are both 000001) at the leftmost position in the current hierarchy, and the row number and the column number where the current grid is located. The calculation method comprises the following steps: YYYYYYY ═ Floor ((Lat + 256)/(512/2)LL))+1，XXXXXX＝Floor((Long+256)/(512/2LL) +1, where Lat, Long are longitude and latitude values of the grid center point, respectively, and LL is the grid layer level. The grid a shown in FIG. 3 is numbered03000006000005, grid b is numbered 04000009000012.
(II) remote sensing image processing
The method mainly processes the remote sensing image needing the shared service, and mainly comprises two contents:
1. remote sensing image metadata extraction
For the remote sensing image taking 'scene' as a unit, key metadata information such as unique image number, image time, sensor name, coverage range, resolution and the like is extracted and stored in a metadata base, and support is provided for subsequently establishing a tile index based on metadata.
2. Slicing process
And taking the established multi-level geographic grid as a slicing reference, carrying out slicing pretreatment and real-time slicing treatment on each scene remote sensing image taking the scene as a unit as required to form a small tile, and storing the small tile in a tile database. Of particular note are:
(1) tile size and format: the slice is made in 256 × 256 format, which may be JPEG, PNG, etc.
(2) And (4) null value part processing: if the tile corresponding to a certain grid of a certain level in the scene remote sensing image cannot completely cover the whole grid (namely the original remote sensing image of the uncovered area is null), the uncovered area of the tile is filled with transparent color.
(3) Tile metadata information recording: while the tile is stored, the unique number, the associated grid number (layer number, row number, column number), the associated original image number, the image time and other information are recorded in the database. These information mainly serve for subsequent metadata-based tile index building, especially where image time is a common condition for tile retrieval, and thus image time information needs to be recorded in the tile information table.
(4) Slicing processing strategy
In order to avoid the problem that the full-level tile pre-cutting needs more processing time and storage space, an optimized caching technology is adopted: generating the most core level tile pre-slicing processing and generating other level tiles through on-demand real-time slicing processing. Wherein figure 4 is a tile request response strategy diagram.
1) Pre-slicing processing of most core level tiles
For each scene remote sensing image, the slice preprocessing of the whole level is not carried out, and the L-th slice preprocessing is carried out at mostMaxHierarchy, LMidHierarchy, LFullAnd (3) slice preprocessing of the 3 most core levels of the levels, generating tiles of the 3 levels of the scene remote sensing image at most in advance, and reading the original remote sensing image for 3 times in the 3 slice processing at most. In particular, if for LFullAnd LMidThe quality requirements of the tiles of two levels are not high, and the tiles of the two levels can be generated not based on remote sensing images but based on LMaxLayer tile generation to improve response speed.
A. L thMaxLayer tile: namely, the tile is suitable for the deepest level of the resolution of the remote sensing image of the scene. This level of tiles has the highest effective resolution and is the basis for the more shallow level of tiles, above which the shallow level of tiles can be generated by resampling the tiles at this level and is also typically the level of tiles actually used in the business application (with the highest effective resolution). It is therefore necessary to pre-generate tiles at this level to improve response speed.
Since the resolution of each remote sensing image may be different, it is not necessary to generate a deep tile (for example, 18 levels) for a low-resolution remote sensing image (for example, 30 meters). L isMaxThe calculation method of (2) is as follows:
LMax＝Floor(Log2(R1/RImg) +1, wherein Floor represents rounding down, R1Representing the length, R, of a 1-pixel representation in a tile at a first levelImgAnd representing the original resolution of the remote sensing image of the scene. For example, according to the above-mentioned geography grid system, for the image with the highest resolution of 1.0 meter, the tile size of 256 × 256 pixels is to be sliced, so that it is suitable for the deepest level L of the slicingMax≈Floor(Log2((256*110*1000/256))/1.0)+1＝19。
B. L thFullLayer tile: namely, the grid level place when the scene remote sensing image just can be completely displayed in a gridA corresponding tile. Generally, when a remote sensing image of a scene is completely displayed without pre-caching, the data of the whole scene needs to be read for display, and the memory and the calculation time are large; in addition, in practical applications, it is often necessary to view the complete coverage and effect of the image, which brings about the need of frequent access to the tile of this level. It is therefore necessary to pre-generate tiles at this level. The method for generating the remote sensing image comprises the steps of reading the scene remote sensing image, slicing, generating the tiles of the level and storing the tiles, wherein the method is the same as A. L isFullThe calculation method of (c) is as follows:
LFull＝Floor(Log2(512/Max(RangeX,RangeY) In) where Floor represents rounding down, Range)XFor the longitudinal width of the remote-sensing image, RangeYThe breadth of the remote sensing image in latitude is Max, and Max is the maximum value.
C. L thMidLayer tile: i.e. tiles of the middle level of the image, LMidIs calculated in the manner of LMid＝Floor((LMax+LFull)/2)+1. Tiles of the hierarchy are pre-generated as appropriate.
If L isMax-LFull>When 4 (experiments show that when the up-resampling factor does not exceed 4, the tile will not have significant distortion, and is therefore set to 4), the hierarchy of tiles needs to be pre-generated. Because the two hierarchies are different greatly at this time, on one hand, if the two hierarchies are generated in real time directly based on available adjacent deep level tiles, the resampling scaling is too large, and the quality of the tiles is affected; on the other hand, when browsing images accessing the middle layer, an excessive middle-level tile is needed to improve the response speed. The generation method is the same as A, B, and the remote sensing image of the scene is read and sliced to generate LMidThe tiles of the hierarchy and saved.
Otherwise, there is no need to pre-generate tiles for the hierarchy. Since then LFullAnd LMaxA small difference of multiples between LFullAnd LMaxThe tiles between the levels can be generated based on real-time calculation, and the calculation cost is low (the two levels are not greatly different) and cannot be matchedResponse speed, etc. have a significant effect. The specific production method is the same as 2).
2) On-demand real-time slicing processing of other hierarchical tiles
Instead of pre-generation, the real-time upsampling calculation generation is carried out on the basis of the available nearest deep-level tile at the first request (the original remote sensing image is not required to be read again, the existing nearest deep-level tile available is read), the real-time generated tiles are stored, and the real-time generated tiles are directly read at the subsequent request without calculation generation again, so that the response speed of the subsequent request is improved.
(III) index building
Based on the above steps, a remote sensing image database of a labeling system is formed, wherein the remote sensing image database takes a smaller 'tile' as an image basic storage unit, takes a more systematic multi-level geographic grid as a data organization index frame of a spatial dimension, and takes richer metadata as a label, and different numbers of tiles (different image numbers, different times and the like) may exist on each grid of each level of grid surface. In order to improve the retrieval efficiency and meet the requirement of more diversified retrieval, mutual bidirectional indexes based on a { geography grid, metadata and tile } triple structure are specially established.
(1) Indexing based on the geogrid: from the geographic grid index to which images (particularly image times therein) the grid relates, which tiles;
(2) video metadata-based indexing: indexing from image-related metadata (such as image time, sensors, resolution, etc.) which geography meshes, which tiles are involved;
(3) indexing of tiles: from tile index to tile is associated with which geographic grid (such as grid number), metadata (such as image time, sensor, resolution, etc.), i.e., a tile index is built from grid information (hierarchy, row number, column number), image metadata (among others).
(IV) service publishing
According to the WTMS standard of the OGC, a map service serving as the WTMS standard is released. The specific logic of the service response is as follows:
1. a user sends an access request with information of a required space range, a required time range, a sensor and the like;
2. after receiving the request, the server analyzes the request and calculates to obtain the related grid level (L)Need) The range of the row and column numbers;
3. the server inquires whether the tiles meeting the conditions exist in a server database in the background according to the analyzed information of the grid hierarchy, the line and row number range, the image time and the like, and returns a tile number list which relates to the grid and respectively meets the conditions:
A. if a tile exists for the grid (the returned list of tile numbers for the grid is not empty), directly reading the tile data and associated metadata information returns based on the tile numbers;
B. if a tile for the mesh does not exist (the returned list of tile numbers for the mesh is empty), the nearest neighbor tile currently available is queried (assuming that the nearest neighbor tile available for the mesh is L)AvaHierarchical tiles);
(1) obtain the corresponding LAvaThe tiles are calculated in real time to generate the required LNeedA hierarchy of tiles.
(2) And returning the generated tile and storing the generated tile and related information thereof in a tile database for subsequent access and direct call.
It should be noted that, when the results in a and B are returned, the tile lists need to be sorted in reverse order according to the grid number and the image time, so that the tiles at different time points involved in the same grid are sorted in reverse order according to time, and the tile at the latest time point is on the upper layer. In addition, in order to increase the response speed and avoid the situation that the data size is too large due to too many tiles meeting the condition, a default parameter Layer can be setMaxWhen the number of tiles is greater than the default parameter LayerMax(e.g., 10), return only the front LayerMaxA tile, i.e. the most recent Layer in timeMaxAnd (4) tiles.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. An image tile map service method based on triple bidirectional indexing and optimized caching is characterized by comprising the following steps:
establishing a multi-level geographic grid system, wherein the multi-level geographic grid system comprises a plurality of layers of geographic grids, and numbering the geographic grids;
extracting metadata of the remote sensing image and storing the metadata into a metadata database;
according to the multi-level geographic grid system and the optimized cache strategy, carrying out slice preprocessing on the remote sensing image to form a tile, and storing the tile in a tile database;
establishing a mutual bidirectional index of triples formed by the geographic grids, the metadata and the tiles;
receiving request information sent by a user, retrieving the request according to the established index, and directly returning the request tile and related metadata information if the request tile exists; and if the request tile does not exist, generating the tile in real time as required, returning the generated tile and related metadata information, and simultaneously storing the tile into a tile database for direct calling and returning when the next request is made.
2. The method of claim 1, wherein the geogrid mesh and the tiles are indexed, the metadata and the tiles are indexed, and the geogrid mesh and the metadata are indexed.
3. The image tile map service method based on triple bidirectional indexing and optimized caching of claim 1, wherein the specific steps for establishing a multi-level geographic grid system are as follows:
extending the longitude range (-180 degrees, 180 degrees) and the latitude range (-90 degrees, 90 degrees) to the geographical range of (-256 degrees, 256 degrees), dividing the geographical range into 4 grids according to a central horizontal line and a central vertical line, wherein the length and the width of each grid are 256-degree squares, and forming a layer 1 grid; on the basis of the grid of the previous layer, gradually dividing each grid of the previous layer into 4 equal parts according to the horizontal central line and the vertical central line of the grid to respectively form 2-N layers of grids;
each mesh in each layer of meshes is uniquely numbered.
4. The method of claim 1, wherein the metadata includes a unique number of the related image, an image time, a sensor name, a coverage area, and a resolution.
5. The image tile map service method based on triple bidirectional indexing and optimized caching as claimed in claim 1, wherein the preprocessing of remote sensing image slices specifically comprises:
taking the established multi-level geographic grid system as a slicing reference, carrying out slicing pretreatment on each scene remote sensing image taking the scene as a unit to form a tile, and storing the tile in a tile database;
and for each remote sensing image, slicing preprocessing is carried out on at most three most core hierarchical tiles, and other hierarchical tiles are generated by utilizing the nearest deep-level tiles available in the database in real time according to the request and are subjected to up-sampling calculation and storage.
6. The method of claim 1, wherein the step of responding to the request comprises:
a user sends request information, wherein the request information contains information such as a required space range, a required time range, a sensor and the like;
after receiving the request information, the server analyzes the request and calculates to obtain the related grid level and the range of the row number and the column number;
the server inquires whether the tiles meeting the conditions exist in a server database in the background according to the analyzed grid level, the line and row number range and the image time information, and returns a tile number list of each grid meeting the conditions:
if the tiles of the grid exist, directly reading tile data and related metadata information to return based on the tile numbers;
if a tile for the grid does not exist, querying the nearest neighboring deep level tile currently available; acquiring a corresponding nearest deep-level tile, and performing calculation processing in real time to generate a tile of a required level; and returning the generated tile, and simultaneously storing the generated tile and related information thereof in a tile database for direct call and return of a subsequent request.
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| CN114092656A (en) * | 2021-11-15 | 2022-02-25 | 中国农业大学 | Remote sensing big data slicing method, device and equipment based on hexagon |
| CN114328599A (en) * | 2021-12-21 | 2022-04-12 | 航天信息股份有限公司 | Method and system for analyzing mapping relation between data standard and constraint object |
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